---------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/claywebb/Dropbox/KU/Research/Sanctions/The Domestic Economic Costs of Sanctions - A Firm Level Analysis/Replication Materials f
> or The Domestic Economic Costs of Sanctions - A Firm Level Analysis/table1.log
  log type:  text
 opened on:  12 Jun 2020, 00:15:26

.   
. * Table 1 Models  
.   
.   * Eli Lilly (1989)
.   
.     * Load data
.           use "rcs.dta"

. 
.     * Generate Returns
.           gen lly_returns = ln(lly_close/lly_close[_n-1])
(3,150 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.           recode tsanction3(.=0)
(tsanction3: 12847 changes made)

.         
.     * Bond Market Crash
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.     * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 5278 to 5634
                delta:  1 unit

.           
.         * Model 1 (LLY 1989)
.           eststo clear

.           
.           eststo: arch lly_returns, ar(4) arch(22)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1027.5883  
Iteration 1:   log likelihood =  1028.0864  
Iteration 2:   log likelihood =  1028.1064  
Iteration 3:   log likelihood =   1028.111  
Iteration 4:   log likelihood =  1028.1124  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1028.1129  
Iteration 6:   log likelihood =  1028.1131  
Iteration 7:   log likelihood =  1028.1131  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       8.11
Log likelihood =  1028.113                        Prob > chi2     =     0.0044

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0017478    .000647     2.70   0.007     .0004798    .0030159
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |  -.1505991    .052894    -2.85   0.004    -.2542694   -.0469288
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L22. |   .0877856   .0481679     1.82   0.068    -.0066218     .182193
             |
       _cons |   .0001705   9.23e-06    18.47   0.000     .0001524    .0001886
------------------------------------------------------------------------------
(est1 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    30.9298
 Prob > chi2(40)           =     0.8477

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.3066
 Prob > chi2(40)           =     0.9933

.           
.     * Model 2 (LLY 1989)
.           eststo: arch lly_returns, ar(4) arch(22) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1048.2891  
Iteration 1:   log likelihood =  1052.2285  
Iteration 2:   log likelihood =  1052.6355  
Iteration 3:   log likelihood =  1052.6624  
Iteration 4:   log likelihood =  1052.6695  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1052.6699  
Iteration 6:   log likelihood =  1052.6715  
Iteration 7:   log likelihood =  1052.6715  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       9.82
Log likelihood =  1052.672                        Prob > chi2     =     0.0017

------------------------------------------------------------------------------
             |                 OPG
 lly_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
lly_returns  |
       _cons |   .0017111   .0005774     2.96   0.003     .0005794    .0028428
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L4. |   -.139918   .0446529    -3.13   0.002    -.2274362   -.0523999
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |  -.2645815   .1617641    -1.64   0.102    -.5816333    .0524702
f13minicrash |   3.221577   1.095786     2.94   0.003     1.073876    5.369278
       _cons |  -8.683645   .1202534   -72.21   0.000    -8.919337   -8.447952
-------------+----------------------------------------------------------------
ARCH         |
        arch |
        L22. |   .1006144   .0415365     2.42   0.015     .0192044    .1820244
------------------------------------------------------------------------------
(est2 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    33.7598
 Prob > chi2(40)           =     0.7460

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    36.4631
 Prob > chi2(40)           =     0.6303

.           
.   * Pfizer (1989)       
.   
.     * Clear
.           clear

.         
.         * Load Data
.       use "rcs.dta"

. 
.     * Generate Returns
.           gen pfe_returns = ln(pfe_close/pfe_close[_n-1])
(3,148 missing values generated)

.     
.         * Sanctions
.           gen tsanction3 = 1 if date > td("04jun1989") & date < td("25may1990")
(12,847 missing values generated)

.           recode tsanction3(.=0)
(tsanction3: 12847 changes made)

.         
.     * Bond Market Crash
.           gen f13minicrash = 1 if date > td("11oct1989") & date < td("17oct1989")
(13,091 missing values generated)

.           recode f13minicrash(.=0)
(f13minicrash: 13091 changes made)

.           
.     * Limit Time Period
.       keep if date > td("30dec1988") & date < td("01jun1990")
(12,737 observations deleted)

.           
.         * Model 3 (PFE 1989)
.           eststo: arch pfe_returns, ar(1) arch(1,3)

(setting optimization to BHHH)
Iteration 0:   log likelihood =   1011.322  
Iteration 1:   log likelihood =  1013.1265  
Iteration 2:   log likelihood =  1013.3812  
Iteration 3:   log likelihood =  1013.4094  
Iteration 4:   log likelihood =  1013.4147  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1013.4161  
Iteration 6:   log likelihood =  1013.4168  
Iteration 7:   log likelihood =  1013.4168  

ARCH family regression -- AR disturbances

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.28
Log likelihood =  1013.417                        Prob > chi2     =     0.5982

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |   .0009538   .0008395     1.14   0.256    -.0006915    .0025992
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0366961   .0696282     0.53   0.598    -.0997727    .1731649
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1724609   .0663032     2.60   0.009     .0425091    .3024128
         L3. |   .0812665   .0377909     2.15   0.032     .0071978    .1553352
             |
       _cons |   .0001581   .0000129    12.24   0.000     .0001328    .0001834
------------------------------------------------------------------------------
(est3 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.1785
 Prob > chi2(40)           =     0.4623

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    21.4039
 Prob > chi2(40)           =     0.9930

.           
.     * Model 4 (PFE 1989)
.           eststo: arch pfe_returns, ar(1) arch(1,3) het(tsanction3 f13minicrash)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  1014.1964  
Iteration 1:   log likelihood =  1017.8352  
Iteration 2:   log likelihood =  1018.6349  
Iteration 3:   log likelihood =  1018.7516  
Iteration 4:   log likelihood =  1018.7788  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  1018.7862  
Iteration 6:   log likelihood =  1018.7876  
Iteration 7:   log likelihood =  1018.7879  
Iteration 8:   log likelihood =  1018.7879  

ARCH family regression -- ARMA disturbances and mult. heteroskedasticity

Sample: 5278 - 5634                               Number of obs   =        357
Distribution: Gaussian                            Wald chi2(1)    =       0.11
Log likelihood =  1018.788                        Prob > chi2     =     0.7446

------------------------------------------------------------------------------
             |                 OPG
 pfe_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
pfe_returns  |
       _cons |    .000988   .0007836     1.26   0.207    -.0005479     .002524
-------------+----------------------------------------------------------------
ARMA         |
          ar |
         L1. |   .0219684   .0674364     0.33   0.745    -.1102045    .1541413
-------------+----------------------------------------------------------------
HET          |
  tsanction3 |   .5708914   .1504905     3.79   0.000     .2759355    .8658472
f13minicrash |   .9289681   1.034033     0.90   0.369    -1.097699    2.955636
       _cons |  -9.129573   .1349934   -67.63   0.000    -9.394155   -8.864991
-------------+----------------------------------------------------------------
ARCH         |
        arch |
         L1. |   .1329522   .0662176     2.01   0.045     .0031681    .2627363
         L3. |   .0598993   .0327959     1.83   0.068    -.0043794     .124178
------------------------------------------------------------------------------
(est4 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    40.5775
 Prob > chi2(40)           =     0.4448

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.8250
 Prob > chi2(40)           =     0.9712

.           
.   * Bed Bath and Beyond 2011
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen bbby_returns = ln(bbby_close/bbby_close[_n-1])
(7,475 missing values generated)

.     
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.                 
.     * Set for analysis
.           tsset t
        time variable:  t, 10825 to 11076
                delta:  1 unit

.           
.         * Model 5 (BBBY 2011)
.           eststo: arch bbby_returns

(setting optimization to BHHH)
Iteration 0:   log likelihood =  650.45019  
Iteration 1:   log likelihood =  650.45019  

Time-series regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  650.4502                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .000655   .0011699     0.56   0.576    -.0016381     .002948
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0003354   .0000173    19.34   0.000     .0003014    .0003694
------------------------------------------------------------------------------
(est5 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    24.6471
 Prob > chi2(40)           =     0.9730

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    13.7070
 Prob > chi2(40)           =     1.0000

.           
.     * Model 6 (BBBY 2011)
.           eststo: arch bbby_returns, het(bmonday debt csanction)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  657.00645  
Iteration 1:   log likelihood =  662.16583  
Iteration 2:   log likelihood =  663.23385  
Iteration 3:   log likelihood =  664.81933  
Iteration 4:   log likelihood =  664.95515  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  665.10577  
Iteration 6:   log likelihood =  665.60414  
Iteration 7:   log likelihood =  665.65863  
Iteration 8:   log likelihood =  665.66171  
Iteration 9:   log likelihood =  665.66208  
Iteration 10:  log likelihood =  665.66209  

Time-series regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  665.6621                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
bbby_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
bbby_returns |
       _cons |   .0008579   .0011134     0.77   0.441    -.0013243    .0030401
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.684005   .8944546     1.88   0.060    -.0690939    3.437104
        debt |  -.4180336   .1484319    -2.82   0.005    -.7089547   -.1271125
   csanction |   .0930033   .1934726     0.48   0.631    -.2861961    .4722027
       _cons |  -8.118579   .0558695  -145.31   0.000    -8.228082   -8.009077
------------------------------------------------------------------------------
(est6 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    22.9440
 Prob > chi2(40)           =     0.9860

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    12.2786
 Prob > chi2(40)           =     1.0000

.           
.   * Gap (2011)
.   
.     * Clear
.           clear

.           
.         * Load Data
.           use "rcs.dta"

.   
.     * Generate Returns
.           gen gps_returns = ln(gps_close/gps_close[_n-1])
(5,759 missing values generated)

.     
.     * Currency Sanctions
.       gen csanction = 1 if date > td("11jul2011") & date < td("18oct2011")
(13,025 missing values generated)

.       recode csanction(.=0)
(csanction: 13025 changes made)

. 
.     * Debt Debate
.       gen debt = 1 if date > td("13may2011") & date < td("01aug2011")
(13,041 missing values generated)

.       recode debt(.=0)
(debt: 13041 changes made)

. 
.     * Black Mondayd             
.       gen bmonday = 1 if date > td("01aug2011") & date < td("15aug2011")
(13,085 missing values generated)

.       recode bmonday(.=0)
(bmonday: 13085 changes made)

.           
.         * Gap Report
.       gen report = 1 if date > td("18may2011") & date < td("24may2011")
(13,091 missing values generated)

.       recode report(.=0)  
(report: 13091 changes made)

. 
.     * Limit Time Period
.       keep if date > td("31dec2010") & date < td("03jan2012")   
(12,842 observations deleted)

.           
.         * Model 7 (GPS 2011)
.           eststo: arch gps_returns

(setting optimization to BHHH)
Iteration 0:   log likelihood =  582.63184  
Iteration 1:   log likelihood =  582.63184  

Time-series regression

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  582.6318                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   -.000702   .0018344    -0.38   0.702    -.0042975    .0028934
-------------+----------------------------------------------------------------
     /SIGMA2 |   .0005745   .0000198    29.01   0.000     .0005357    .0006134
------------------------------------------------------------------------------
(est7 stored)

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    37.5971
 Prob > chi2(40)           =     0.5790

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =     4.8936
 Prob > chi2(40)           =     1.0000

.           
.     * Model 8 (GPS 2011)
.           eststo: arch gps_returns, het(bmonday debt csanction report)

(setting optimization to BHHH)
Iteration 0:   log likelihood =  621.30239  
Iteration 1:   log likelihood =  629.18233  
Iteration 2:   log likelihood =  633.63955  
Iteration 3:   log likelihood =  634.95762  
Iteration 4:   log likelihood =  636.60809  
(switching optimization to BFGS)
Iteration 5:   log likelihood =  637.66847  
Iteration 6:   log likelihood =  638.92119  
Iteration 7:   log likelihood =  639.31659  
Iteration 8:   log likelihood =  639.35841  
Iteration 9:   log likelihood =  639.36063  
Iteration 10:  log likelihood =   639.3607  
Iteration 11:  log likelihood =   639.3607  

Time-series regression -- multiplicative heteroskedasticity

Sample: 10825 - 11076                             Number of obs   =        252
Distribution: Gaussian                            Wald chi2(.)    =          .
Log likelihood =  639.3607                        Prob > chi2     =          .

------------------------------------------------------------------------------
             |                 OPG
 gps_returns |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gps_returns  |
       _cons |   .0004887   .0011487     0.43   0.670    -.0017627    .0027402
-------------+----------------------------------------------------------------
HET          |
     bmonday |   1.951886    .503428     3.88   0.000     .9651855    2.938587
        debt |  -.2125711    .174265    -1.22   0.223    -.5541242     .128982
   csanction |   .3955918   .1929226     2.05   0.040     .0174705    .7737131
      report |    3.91351   .8339313     4.69   0.000     2.279035    5.547985
       _cons |  -8.092062   .0840613   -96.26   0.000    -8.256819   -7.927305
------------------------------------------------------------------------------
(est8 stored)

.           
.           drop e v s se se2

.           
.           predict e, residuals

.       predict v, variance

.       gen s = sqrt(v)

.       gen se = e/s

.       gen se2 = se^2

.       
.           ac se

.           pac se

.           wntestq se

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    31.5084
 Prob > chi2(40)           =     0.8290

.           
.       ac se2

.       pac se2

.       wntestq se2

Portmanteau test for white noise
---------------------------------------
 Portmanteau (Q) statistic =    28.9173
 Prob > chi2(40)           =     0.9032

.           
.   * Generate Table
.     esttab, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanction3 Sanction1 f13minicrash Mi
> niCrash csanction Sanction2 debt DebtDebate bmonday BlackMonday) nomtitles title(Within Sectors Within Years) nodep

Within Sectors Within Years
------------------------------------------------------------------------------------------------------------------
                 (1)          (2)          (3)          (4)          (5)          (6)          (7)          (8)   
------------------------------------------------------------------------------------------------------------------
main                                                                                                              
Constant       0.002***     0.002***     0.001        0.001        0.001        0.001       -0.001        0.000   
             (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.001)      (0.002)      (0.001)   
------------------------------------------------------------------------------------------------------------------
ARMA                                                                                                              
L4.ar         -0.151***    -0.140***                                                                              
             (0.053)      (0.045)                                                                                 
L.ar                                     0.037        0.022                                                       
                                       (0.070)      (0.067)                                                       
------------------------------------------------------------------------------------------------------------------
ARCH                                                                                                              
L22.arch       0.088*       0.101**                                                                               
             (0.048)      (0.042)                                                                                 
L.arch                                   0.172***     0.133**                                                     
                                       (0.066)      (0.066)                                                       
L3.arch                                  0.081**      0.060*                                                      
                                       (0.038)      (0.033)                                                       
Constant       0.000***                  0.000***                                                                 
             (0.000)                   (0.000)                                                                    
------------------------------------------------------------------------------------------------------------------
HET                                                                                                               
Sanction1                  -0.265                     0.571***                                                    
                          (0.162)                   (0.150)                                                       
MiniCrash                   3.222***                  0.929                                                       
                          (1.096)                   (1.034)                                                       
BlackMon~y                                                                      1.684*                    1.952***
                                                                              (0.894)                   (0.503)   
DebtDebate                                                                     -0.418***                 -0.213   
                                                                              (0.148)                   (0.174)   
Sanction2                                                                       0.093                     0.396** 
                                                                              (0.193)                   (0.193)   
report                                                                                                    3.914***
                                                                                                        (0.834)   
Constant                   -8.684***                 -9.130***                 -8.119***                 -8.092***
                          (0.120)                   (0.135)                   (0.056)                   (0.084)   
------------------------------------------------------------------------------------------------------------------
SIGMA2                                                                                                            
Constant                                                           0.000***                  0.001***             
                                                                 (0.000)                   (0.000)                
------------------------------------------------------------------------------------------------------------------
N                357          357          357          357          252          252          252          252   
aic        -2048.226    -2093.343    -2016.834    -2023.576    -1296.900    -1321.324    -1161.264    -1266.721   
bic        -2032.715    -2070.077    -1997.445    -1996.432    -1289.842    -1303.677    -1154.205    -1245.545   
------------------------------------------------------------------------------------------------------------------
Standard errors in parentheses
* p<.10, ** p<.05, *** p<.01

.     esttab using rawtables/table1raw.tex, b(3) se(3) star(* .10 ** .05 *** .01) scalars(aic bic) nogaps compress coeflabels(_cons Constant tsanct
> ion3 Sanction1 f13minicrash MiniCrash csanction Sanction2 debt DebtDebate bmonday BlackMonday) nomtitles title(Within Sectors Within Years) nodep
(output written to rawtables/table1raw.tex)

.         
. * Close Log File
.   log close     
      name:  <unnamed>
       log:  /Users/claywebb/Dropbox/KU/Research/Sanctions/The Domestic Economic Costs of Sanctions - A Firm Level Analysis/Replication Materials f
> or The Domestic Economic Costs of Sanctions - A Firm Level Analysis/table1.log
  log type:  text
 closed on:  12 Jun 2020, 00:15:56
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